Production of hydrocarbons with test separator

ABSTRACT

An improved solution for estimating the flow rate of a fluid in the wells of a hydrocarbon production installation, during production. A hydrocarbon production installation can include at least two production wells, each production well including at least one device adapted for providing an estimate of the flow rate of a fluid in the well based on respective data, the wells being connected to a test separator adapted for providing an estimate of the cumulative flow rate of the fluid for all of the wells based on respective data. Further, during production, a data reconciliation and validation process can involve the data relative to the devices and the test separator.

The present invention relates to the field of hydrocarbon production, and more specifically a production method on a production line comprising at least two production wells.

Hydrocarbon production installations comprise lines in which fluids flow, in particular production lines comprising a production well and in which hydrocarbons flow from a hydrocarbon tank toward a wellhead. It is common today to try to provide an estimate of the flow rate of the fluid flowing in such production lines. Indeed, in a known manner, the supplied flow rate estimate allows better management of the line. One thus obtains better oversight or better knowledge of the overall production of the installation, owing to the estimate potentially supplied for each line, typically on a line-by-line basis.

One approach consists of looking for an estimate of the fluid flow rate via a device and based on data relative to the device, using a thermodynamic model provided to that end, the device (integrated in the line) and the respective data initially not being provided for that purpose. One then obtains a “virtual” counter. In this context, some solutions use several models of this type, based on data relative to these different devices, and each model theoretically provides a different estimate of the flow rate for equivalent given production conditions (e.g., gas/oil/water fractions). Typically, a maximum of two or three models are chosen and the data relative to the production conditions (water/oil/gas fraction) are corrected so that the flow rate estimates are similar. This approach is not fully satisfactory, in that the supplied estimate of the flow rate is not close enough to the actual flow rate.

Another approach consists of using a metric counter positioned in the line for which one wishes to determine the flow rate. Metric counters are devices adapted for taking a measurement on the fluid flowing in the line, and directly providing an estimate of the flow rate of the fluid in the line based (at least) on the measurement. The known metric counters also use the value of at least one parameter, commonly called “calibration parameter”. To that end, a metric counter can in particular take one or several physical measurements (e.g., electric, nuclear and/or optical, for example permittivity, conductivity and/or gamma attenuation measurements). Such a metric counter may, for various reasons, provide an estimate that is not fully satisfactory. For example, it is necessary for as accurate a value as possible to be assigned to the calibration parameters, failing which the estimate of the flow rate of the fluid in the line provided by the metric counter would be too far removed from reality, and therefore unusable. This is especially true when we consider flare gas counters or fiscal counters, for which it is particularly critical to obtain an estimate closely corresponding to reality. Yet the calibration parameter can often be erroneous, for example due to a drift of the sensors.

Still another approach consists of using a test separator to “test” a production line, i.e., to measure the flow rate of a fluid in a production line periodically. However, test separators themselves may provide inaccurate results, even though they are relatively precise. Furthermore, for operational and economic reasons (production loss limitations), several wells are generally tested at the same time (for example, three to four wells). One thus obtains information on the oil, water and gas flow rates by tested riser, but not by tested well. Means exist for recovering this information with flow rate information by well, but these means currently only make it possible to obtain an estimate that is still relatively distant from reality.

The aim of the present invention is to provide an easy-to-implement solution for estimating the flow rate of a fluid in each well of a hydrocarbon production line, during production, as accurately as possible.

To that end, the present invention proposes a method for producing hydrocarbons on a line of a hydrocarbon production installation comprising at least two production wells, each production well comprising at least one device adapted for providing an estimate of the flow rate of a fluid in the well based on respective data, the wells being connected to a test separator adapted for providing an estimate of the cumulative flow rate of the fluid for all of the wells based on respective data. During production, the method comprises determining the data respective to the provision of an estimate of the flow rate by the device of each well; determining the data respective to the provision of an estimate of the cumulative flow rate by the test separator; and a data reconciliation and validation process involving the determined data, the reconciliation being conditioned by an at least substantial equality between the sum of the estimates of the flow rate of the fluid to be provided by the device of each well and the estimated cumulative flow rate to be provided by the test separator.

The invention also proposes a computer program, recordable on a data storage memory, comprising instructions for carrying out the method.

The invention also proposes a system adapted for communicating with devices comprised in at least two production wells of a hydrocarbon production line, the devices being adapted for providing an estimate of the flow rate of the fluid in the well based on respective data, and with a test separator connected to the wells and adapted for providing an estimate of the cumulative flow rate of the fluid for all of the wells based on respective data, the system comprising a memory having recorded the program.

The invention also proposes a hydrocarbons production installation, comprising a production line comprising at least two production wells, each production well comprising at least one device adapted for providing an estimate of the flow rate of a fluid in the well based on respective data, the wells being connected to a test separator adapted for providing an estimate of the cumulative flow rate of the fluid for all of the wells based on respective data, and the system, the system being adapted for communicating with the devices and the test separator of the production line.

According to preferred embodiments, the invention comprises one or more of the following features:

-   -   the data respective to the provision of an estimate of the         cumulative flow rate via the test separator comprise at least         one measurement done by a sensor of the test separator;     -   the test separator comprises at least one metric counter, the         sensor taking the measurement being a sensor of the metric         counter;     -   the test separator comprises several single-phase metric         counters each adapted for providing an estimate of the flow rate         of a respective phase of the fluid based on a measurement taken         by a respective sensor;     -   the fluid comprises a water phase, a gas phase and an oil phase;     -   at least one production well comprises a metric counter adapted         for providing an estimate of the flow rate of the fluid in the         well based on respective data including a measurement done by at         least one sensor of the metric counter on the fluid;     -   a production well comprising a metric counter further comprises         at least one other device adapted for providing an estimate of         the flow rate of the fluid in the well based on respective data         that are determined and involved in the data reconciliation and         validation process;     -   the other device is a choke, a device associated with an inflow         module, and/or a pipe;     -   the data reconciliation and validation process minimizes a cost         function penalizing, for each reconciled datum, the difference         between its value before reconciliation and its reconciled         value;     -   the difference is weighted in the cost function by an         uncertainty relative to said reconciled datum;     -   the method further comprises a detection of any reconciled datum         for which the difference between its value before reconciliation         and its reconciled value exceeds a threshold depending on the         uncertainty relative to the reconciled datum;     -   the method comprises reiterating the data reconciliation and         validation method, removing the reconciled datum with the         greatest weight in the value of the cost function, as long as         the value of the cost function is above a predetermined         threshold;     -   the method further comprises providing the reconciled value of         the fluid flow rate in each well;     -   the method further comprises providing an uncertainty relative         to the reconciled value of the fluid flow rate in each well         depending on the difference between its value before         reconciliation and its reconciled value;     -   the measurement done by a metric counter sensor on the fluid is         an electric, nuclear and/or optical measurement;     -   the choke is adapted for providing an estimate of the flow rate         of the fluid based on a valve opening and a measurement done by         at least one sensor on the fluid supplying the pressure losses         associated with the valve opening;     -   the device associated with the inflow module is adapted for         providing an estimate of the flow rate of the fluid based on a         pressure measurement done by at least one sensor on the fluid         and physicochemical properties of the fluid; and/or     -   the pipe is adapted for providing an estimate of the flow rate         of the fluid based on geometric and/or mechanical properties of         the pipe and a measurement done by at least one sensor on the         fluid supplying the pressure losses associated with the         geometric and/or mechanical properties of the pipe.

Other features and advantages of the invention will appear upon reading the following description of one preferred embodiment of the invention, provided as an example and in reference to the appended drawing.

FIG. 1 shows a diagram of the method.

FIGS. 2 and 3 illustrate the operation of one example metric counter.

FIG. 4 shows a diagram of one example of a hydrocarbon production installation.

FIG. 5 shows an example production well.

FIG. 1 shows the hydrocarbon production method. The hydrocarbon production method is carried out on a production line comprising at least two production wells. Thus, a fluid (i.e., the produced hydrocarbon) flows in each well, potentially at a different flow rate. Each production well comprises at least one device adapted for providing an estimate of the flow rate of the fluid in the well based on respective data (for each device). The wells are also connected to a test separator adapted for providing an estimate of the cumulative flow rate of the fluid for all of the wells based on respective data (at the test separator). During production, the method comprises determining S10 data relative to the provision of an estimate of the flow rate by the device of each well. The method also comprises determining S12 data relative to the provision of an estimate of the cumulative flow rate by the test separator. The method lastly comprises a data reconciliation and validation (DVR) process S20 involving the determined data. The reconciliation is conditioned by an at least substantial equality between the sum of the estimates of the flow rate of the fluid to be provided by the device of each well and the estimated cumulative flow rate to be provided by the test separator. The method of FIG. 1 makes it possible to estimate the flow rate of the fluid in each well easily and relatively accurately (i.e., with a result relatively close to the actual value of the flow rate).

Indeed, the method fits into the context where each well incorporates at least one device adapted for supplying the flow rate of the fluid flowing in the well. One thus already has an estimate of the flow rate by well. Furthermore, the method also provides a test separator adapted for providing an estimate of the cumulative flow rate, i.e., for all of the wells connected to the test separator. This thus allows redundancy of information, at a lower operational cost, given that several wells are connected to the test separator. Typically, a number greater than or equal to three wells, and/or less than or equal to ten wells, or even five wells, for example three or four wells, is connected to the test separator. This informational redundancy makes it possible to improve the first item of information provided for each well, particularly when a test separator provides a relatively precise estimate. However, the method goes further, since it implements a DVR process that calls into question not only the estimates of each well, but also the estimate provided by the test separator, unlike a traditional approach, where this last estimate is trusted fully. Furthermore, the DVR process involving the data relative to the provision of an estimate by each device, including the test separator (and not only the estimates provided in fine), these data are challenged directly, which makes it possible to obtain a better estimate.

In particular, these data may comprise measurements that improve the estimate by being called into question.

For example, the data relative to the provision of an estimate of the cumulative flow rate via the test separator and determined in S12 may comprise at least one measurement done by a sensor of the test separator. Indeed, as known by those skilled in the art, a test separator is a device comprising a chamber with an intake inlet for a fluid (optionally coming from several wells) in the chamber, means for separating the different phases (e.g., water, oil and gas) of the fluid (for example, a sufficient emission volume of the chamber and a geometry for naturally decanting the fluid), outlets arranged based on the geometry of the chamber to make it possible to discharge each of the various phases of the fluid from the chamber separately, and tools making it possible to determine the flow rate of each phase. These tools generally comprise a sensor making it possible to determine the outlet flow rate of each phase. This may be the sensor of a flowmeter, or more generally of a metric counter. Thus, the test separator may comprise several single-phase metric counters each adapted for providing an estimate of the flow rate of a respective phase of the fluid based on a measurement taken by a respective sensor. The metric counters of the separator may traditionally consist of any combination of mass counters, in which case the sensor may be a Coriolis sensor, differential pressure counters, then including a plate with orifices, or ultrasonic counters. Thus, the raw measurements done by the sensors of any type of metric counter of the test separator are also challenged in the DVR process S20, unlike a naïve approach, where the flow rate values provided by the test separator would be the data incorporated into the DVR process.

Furthermore, at least one production well may also comprise a metric counter. In this case, the metric counter of a well is adapted for providing an estimate of the flow rate of the fluid in the well based on respective data determined in S10. These data include a measurement done by at least one sensor of the metric counter on the fluid. Furthermore, any well may comprise at least one device other than a metric counter also adapted for providing an estimate of the flow rate of the fluid in the well based on respective data, these data (which may also comprise measurements) then also being able to be determined and involved in the data reconciliation and validation process. To that end, one may simply add a condition to the DVR process, i.e., a condition of an at least substantial equality between the various flow rate estimates supplied for a same well. As in the case of the test separator, challenging the measurement from the metric sensor determined in S10 in the DVR process S20 rather than, naïvely, the estimate provided by the metric counter, improves the result.

It should be noted that if a well has several devices each adapted for providing the flow rate in the well, for example including a metric counter, one then has a second layer of redundancy (in addition to that offered by the test separator), which improves the estimate of the flow rate provided for the well at the end of the reconciliation. Furthermore, the method can be repeated at different moments, the data determined in S10 and S12 for each moment then potentially being reconciled within a single global reconciliation. This makes it possible to use the temporal redundancy to improve the estimate, in particular if time constraints are inserted into the DVR process S20. Lastly, the expression “metric counter”, understood by one skilled in the art, refers to the aforementioned devices. Any metric counter used by the method, including a metric counter of the test separator, can therefore in particular, in order to provide a flow rate estimate, take one or several physical measurements on the fluid (e.g., electric, nuclear and/or optical, for example permittivity, conductivity and/or gamma attenuation measurements), and the counter can use predetermined values of calibration parameters and/or predetermined fluid hypotheses.

The DVR done by the method can be implemented within a more global, improved hydrocarbon production method, said production being done by the hydrocarbon production installation incorporating the line. The method may for example comprise providing, for example to a data warehouse, the reconciled value of the fluid flow rate in each well, and also possibly the reconciled value of the cumulative flow rate, i.e., a value corresponding to the estimates of the flow rate provided by the various devices and by the test separator once their respective data are reconciled, knowing that the sum of the estimates of the flow rate provided for each well is at least substantially equal to the estimate of the cumulative flow rate, by the very definition of the reconciliation process. This makes it possible for specialists given the reconciled values to study the production. The method may also comprise providing an uncertainty relative to each reconciled fluid flow rate value. The uncertainty may depend on the difference between the value of the flow rate before reconciliation and the reconciled value of the flow rate. The value before reconciliation can be the value provided, for example by a metric counter or another device based solely on the data determined in S10 or S12, without taking account of the reconciliation (this estimate without reconciliation, therefore relatively erroneous, may be computed in parallel with the reconciled value derived from the DVR process). Thus, the method may provide an uncertainty by device providing a respective estimate, based on the difference between the value before reconciliation supplied by the device and the reconciled value. This makes it possible to detect the devices providing the least accurate estimates, for example with traditional comparisons between the various supplied values. Thus, the method of FIG. 1 allows better management of the line, the installation, and therefore the production.

As explained above, the estimate of the flow rate in a production well of a hydrocarbon production installation in particular owing to the integration of the metric counter in the well allows better management of the production line, with better oversight or better knowledge of the overall production of the installation, e.g., in real time and/or on a line-by-line basis. The method of FIG. 1 then makes it possible to improve the estimates provided for each line on which it is implemented, which improves the overall management of the production. Thus, the method can be implemented during production on several lines each grouping together several wells, steps S10, S12 and S20 being carried out for each line independently of the other lines. The flow rate estimates provided for each well of each line following the reconciliation can then be centralized to manage the production.

Owing to the test separator, the method makes it possible to determine precise flow rates (oil, water and gas) by well in all cases. Furthermore, in one example, the method makes it possible to validate and potentially calibrate multiphase counters situated on each well. The method can also make it possible to calibrate parameters of flow rate estimating models that will be outlined later (for example, choke equation, or pressure drop model in the tubing of the well, or pump curves). The method may also make it possible to identify faulty sensors and/or provide reliable replacement values if the multiphase counters are erroneous. Relatively speaking, the method does not experience the typical limits, for example a potential need to perform operations on the well, a possible influence of mixed compositions, since several wells are tested at the same time, a method precision potentially not taking into account uncertainties regarding the reference measurements when computing its coefficient, a potential residence time of the fluids (since when the production of a single well varies, the fact that the generated variation will only be effective after certain amount of time must be taken into account), a potential attenuation of the generated variation due to the production of other fluids and flow regimes, and any transition generated by the production variations of the well. In one example, the described solution considers all of the modeled measurements, models and parameters of the well and the test reference (e.g., a test separator at the surface treatment level) and takes into account their respective uncertainties in order to provide an oil, water and gas flow rate by tested well as well as their respective uncertainties. Due to the (potential) use of the temporal redundancy, it also makes it possible to identify faulty sensors, reliabilize the measurements from multiphase counters, and find the proper configuration of the models for estimating flow rates on the wells.

The fluid whose flow rate is estimated can have a single phase, and in this case, estimating the flow rate of the fluid in the line simply consists of providing a value for the flow rate of the sole phase of the fluid. This is typically the case for estimates done by the test separator, as previously explained, since the initially multiphase fluid is separated into its different phases, then each making up a single-phase fluid. A counter can perform this estimate based on any model using a measurement on the fluid by at least one sensor of the counter. The model may further involve the value of at least one calibration parameter that corresponds to the measurement of the sensor under a predetermined flow condition of the fluid (for example, a predetermined flow rate or flow speed).

Alternatively, the fluid whose flow rate is estimated may be a multiphase fluid. This is typically the case in a production well, since the produced hydrocarbons typically comprise a water phase, a gas phase and an oil phase. In this case, estimating the flow rate of the fluid in the line may consist of providing the information making it possible to determine the flow rate of each phase. This information may directly comprise an estimate of the flow rate of each phase, and/or an estimate of the fraction (e.g., instantaneous) of each phase in the multiphase fluid in addition to the cumulative flow rate (e.g., instantaneous) of the multiphase mixture in the line. In the second case, it suffices simply to multiply the fraction of a respective phase and the total flow rate to obtain the flow rate of the respective phase. In the case of a multiphase fluid, a metric counter adapted for providing such an estimate is referred to as a multiphase flow meter (MPFM). This may for example be a mass flow sensor or an analyzer. Such a counter involves complex technologies, and the measurements given by the sensor(s) included in such a counter are in particular highly sensitive to the properties of the fluids. Consequently, the counter often does not use the correct calibration parameters and/or some of the sensors may drift more quickly than anticipated, causing the counter to provide a relatively inaccurate value of the fluid flow rate. The method of FIG. 1 makes it possible to offset this problem.

The multiphase counter may in particular work using the following principle, described in reference to FIGS. 2-4. A multiphase counter of the test separator may operate on a similar principle, as one skilled in the art can assess. In particular, the equations presented below are used mutatis mutandis, with the exception that the index representing the phase (three possible values in the equation below: water, gas or oil) is eliminated, since a single-phase counter only operates on one phase.

The sensor of the metric counter may be a traditional gamma sensor, sending waves through the fluid at the sensor and measuring the mass attenuation of the wave. The sensor may be adapted for sending several energy levels, so as to allow sufficient information crosschecking for a multiphase fluid. A single energy level may be used for a single-phase counter. Below, α denotes the linear attenuation in m−1, μ denotes the mass attenuation in m²/kg, and ρ denotes the volume density in kg/m3. By definition, α=μ*ρ. The mass attenuation μ depends on the compound and the gamma energy level.

For the hydrocarbon fluid in the above example (a water phase, an oil phase and a gas phase), it suffices for the sensor to be capable of taking at least two measurements (e.g., corresponding to two different energy levels). These measurements are then part of the data involved in the DVR process of the method of FIG. 1. Traditionally, reference is made to low energy level and high (or mean) energy level, respectively corresponding to indices 1 and 2 in the notations below. The calibration parameters then comprise an attenuation value for each pure phase, per energy level, which can be denoted μgas_1, μgas_2, μwater_1 and μwater_2, and they can also be involved in the DVR process. Thus, μph_i is the mass attenuation μ measured by the sensor for energy level i, in the presence only of phase ph.

It should be noted that if, for a given hydrocarbon production installation, the salinity of the wells is different from one well to another, these parameters may then vary. Conversely, two wells with the same salinity should have the same water attenuation values for a same production installation. Furthermore, it should be noted that the sensor can in fact also measure a third energy level, for example even higher than the other two. At such an energy level, and in general at particularly high energy levels, the various compounds absorb substantially the same quantity of energy (this can be reflected by the fact that the mass attenuations are approximately equal at this energy level). A counter may, however, use this energy to validate its estimate of the flow rate and/or the water, oil and gas fractions provided based on the other two energy levels, preferably with a high uncertainty. It is also possible to link the measurement done for this third energy level to the density of the mixture by a line obtained after calibration.

Owing to the knowledge of the parameters μ_(gas) _(_) ₁, μ_(gas) _(_) ₂, μ_(oil) _(_) ₁, μ_(oil) _(_) ₂, μ_(water) _(_) ₁ and μ_(water) _(_) ₂, it is possible to outline a so-called “calibration triangle” based on the following six values:

α_(gas) _(_) ₁=μ_(gas) _(_) ₁*ρ_(gas)

α_(gas) _(_) ₂=μ_(gas) _(_) ₂*ρ_(gas)

α_(oil) _(_) ₁=μ_(oil) _(_) ₁*ρ_(oil)

α_(oil) _(_) ₂=μ_(oil) _(_) ₂*ρ_(oil)

α_(water) _(_) ₁=μ_(water) _(_) ₁*ρ_(oil)

α_(water) _(_) ₂=μ_(water) _(_) ₂*ρ_(oil)

The volume densities ρ are variables that depend on the pressure P and temperature T values of the line where the counter is operating and the composition of the fluid, information also provided (e.g., by a measurement done by dedicated sensors, e.g., by other devices, for example one or several of those adapted for providing another estimate of the flow rate in the well and previously mentioned, in particular for example for P and T, or by a predetermined value, in particular for example for the GOR—“gas/oil ratio” of the hydrocarbon, excluding water—and/or the salinity, which are predetermined fluid hypotheses), and therefore known by the metric counter, which can therefore deduce the volume densities ρ therefrom. Thus, if the operating conditions P and T change and/or if the composition of the fluid (GOR, salinity) changes, the calibration triangle is also modified, since the gas, oil and water densities vary. The linear attenuations thus also vary. In this sense, the volume densities are calibration parameters that in particular correspond to predetermined fluid hypotheses (GOR and salinity). Some or all of these data can also be part of the data involved in the DVR process.

FIG. 2 schematically shows a calibration triangle 20 corresponding to such a calibration, with coordinate apices (α_(gas) _(_) ₁, α_(gas) _(_) ₂), (α_(oil) _(_) ₁, α_(oil) _(_) ₂), and (α_(water) _(_) ₁, α_(water) _(_) ₂), therefore corresponding to the six values listed above.

FIG. 3 shows an example of the counter: the counter 30. To perform the calibration, the counter 30 can receive a sample 34 of each of the (only one phase in the case of a single-phase counter). Next, in S20, the counter 30 uses a sensor 32 provided to that end to measure the mass attenuations of each of the phases, which makes it possible to determine S30 the calibration parameters (i.e., the linear attenuations, and/or the mass attenuations themselves, depending on the selected point of view, knowing that one or the other of these data can be saved by the counter as calibration parameter, subject to next performing the appropriate computation). During use, the counter 30 measures a mass attenuation of the fluid (which is then a multiphase mixture). The detector of the sensor 32 measures e and saves “counts” Nicounts for each energy level (at least the “low” 1 and “high” 2 levels, and potentially an even higher level as well).

Owing to the following equations:

N ^(i) _(counts) =N ^(i) ₀*decay*exp[−(x _(water)*μ_(water-i)*ρ_(water) +x _(gas)*μ_(gas-i)*ρ_(gas) +x _(oil)*μ_(oil-i)*ρ_(oil))xDthroat]

-   -   for i=1 and 2, and

x _(water) +x _(gas) +x _(oil)=1,

-   -   with x_(water), x_(gas) and x_(oil) designating the fractions of         each of the phases,     -   the volume densities ρ that are predetermined and given by the         composition and the thermodynamics, and N^(i) ₀, decay and         Dthroat that are known,     -   the counter 30 can determine the instantaneous fractions of each         of the phases.

A computer program can be provided to carry out the method. In a known manner in computers, this program is able to be saved in a data storage memory and may comprise instructions for carrying out the method, in particular steps S10, S12 and S20. Thus, any computer system may comprise a memory having saved the program. In all cases, the computer program is provided to command the execution of the method by a system provided to that end. The system (which may be one of the devices, for example the test separator) is adapted for communicating with all of the devices involved in the method. In a known manner in computers, this program is able to be saved in a data storage memory and may comprise instructions. Thus, the computer program is saved by a memory of the system. The computer program may comprise any type of instructions known in computing. These instructions may be lines of code, written in a computer language, for example object-oriented, possibly in the form of source code, compiled code or precompiled code. It may also involve an installation program (i.e., making a system adapted for carrying out the method or commanding the execution thereof). The program may be tangibly saved on a storage memory adapted for that purpose. It may involve a volatile or nonvolatile memory, for example EPROM, EEPROM, flash memory or CD-ROM.

FIG. 4 shows an example of a hydrocarbon production installation comprising several metric counters, in which the method can be implemented.

The installation 60 comprises a hydrocarbon production line, made up of several pipes in which the fluid flows. The production line in particular comprises several fluid production wells 78 emerging from the hydrocarbon tank 66. Three production wells 78 are shown in the figure, but any number adapted for optimal coverage of the tank 66 can be configured to account for the geological complexity of the tank, the quality of the fluids present in the tank, the geographical location of the tank (on land, at sea, very deep sea), and inherent constraints. The production line comprises a fluid pipe 74 supplied with fluid by a manifold 40. The fluid production pipe 74 is a main receiving the fluid from all of the production wells 78 drilled in the tank 66 via a manifold (here, the manifold 40), acting as a unifier. The fluid production pipe 74 is situated on a seabed 64, and supplies a riser 72 (i.e., a substantially vertical pipe) leading to a main station, e.g., in the case at hand, a floating production, storage and offloading (FPSO) unit 68 situated on the marine surface 62. The installation 60 also comprises an injection line comprising several injection wells 79, and operating according to a principle symmetrical to that of the production line.

The installation 60 also incorporates (i.e., comprises) a metric counter 50 as previously described for each production well 78 and each injection well 79. The metric counter 50 is shown in the figure upstream from the wellheads 44, each time on a pipe of the line, but it may be in any other appropriate location as assessed by one skilled in the art. Furthermore, the installation 60 could comprise a quantity of metric counters 50 lower or higher than that shown in the figure, depending on the operating needs. The installation 60 also comprises other devices 52 positioned on different lines in any location appropriate for their function (not outlined here), each being adapted for providing a second estimate of the flow rate in the line where it is installed in addition to its function. Other estimates for each well could be provided by other devices. The installation also comprises a test separator 80 connected to the three illustrated production wells 78, and adapted for deviating the production fluid coming from the manifold 40 in order to estimate the flow rate thereof, phase by phase, thus providing an estimate of the cumulative flow rate of the three production wells 78. In the figure, the test separator 80 is shown on the pipe downstream from the manifold 40, but this illustration is of course only schematic, with the understanding that the test separator 80 can be placed in any location making it possible to collect the production of several production wells 78 and that it can be moved as desired to successively test other groups of production wells 78. Thus, the installation 60 is adapted for carrying out the method of FIG. 1 with the devices 50 and/or 52 of the production wells 78 and the test separator 80, either by the test separator 80 itself, which then has a program comprising instructions to that end and is adapted for communicating with the devices 50 and 52, or by a system adapted for communicating with the devices 50, 52 and 80. The installation 60 allows better hydrocarbon production, owing to a good subsequent estimate of the flow rates.

The DVR process S20 will now be discussed.

The data reconciliation and validation (DVR) process involves the data determined in S10 and S12. This means that the DVR process calls all of these data into question. Indeed, the data “involved” in the DVR process are, by definition, the variables of the DVR process. The reconciliation is conditioned by an equality (e.g., at least substantial, i.e., with a difference below a predetermined, or zero, error) between the estimates of the flow rate of the fluid to be provided by the device of each well and the estimate of the cumulative flow rate to be supplied by the test separator. In other words, the DVR is done on the basic hypothesis that the sum of the estimates of the fluid flow rate to be provided for each well should be equal to the estimated cumulative flow rate.

DVR is a known process making it possible to provide input values (i.e., the involved data, e.g., respective measurements from the devices, including the measurement by the sensor of the metric counter, and in fine reconciled by the process) and to modify these values based on predetermined uncertainty intervals and predetermined constraints, to comply with one or several condition(s) directly or indirectly involving the values in question. This may be done effectively by minimizing a cost function penalizing, for each reconciled datum, the difference between its value before reconciliation and its reconciled value. Furthermore, for a finer use of the DVR, the difference can be weighted in the cost function by an uncertainty relative to said reconciled datum. The greater the uncertainty, the smaller the penalty associated with the difference between the value before reconciliation and the reconciled value, since the difference is more “expected” in theory. This uncertainty can be determined in any manner, for example based on predetermined knowledge, for example from the geologist or the builder of the device. Thus, particularities are taken into account related to each of the data involved in the DVR process. For example, the uncertainties relative to the data with respect to the test separator and determined in S12 can be below the uncertainties related to the other data (determined in S10). Indeed, the test separator being provided for periodic use, it is generally more reliable than the permanent devices of the installation. A builder uncertainty may also be predetermined for the test separator, this uncertainty being lower than the predetermined builder uncertainty for multiphase well counters.

Thus, the DVR process may consist in general of resolving the following minimization program:

${Min}{\sum\limits_{i}\left( \frac{k_{i}^{*} - k_{i}}{e_{i}} \right)^{2}}$

-   -   with k_(i) the data involved in the process (i.e., the data to         be reconciled),     -   e_(i) the uncertainty of the datum i,     -   k_(i)* the reconciled value of the datum i,     -   e_(i)* the reconciled uncertainty of the datum i,     -   y_(j) the variables not measured and therefore for example         computed,     -   and the term (k_(i)*˜k_(i)/e^(i))² is called “penalty”,     -   under the aforementioned constraint and linking the variables         k_(i) and y_(j).

Indeed, the method carries out a DVR to “equalize” certain values. Specifically, the data determined in S10 and S12 are theoretically supposed to yield an equality between a sum of the estimates of the fluid flow rate for each respective well and an estimate of the cumulative flow rate for the test separator. Thus, the sum of the penalties is minimized by at least substantially respecting this equality constraint. Furthermore, the DVR method may comprise one or several inequality constraints of type G(k_(i)*, y_(j))>0, which for example corresponds to the bounds of the system (e.g., the fact that a pressure must be greater than 0, that a ratio, such as the water-cut, must be below 100%). This makes it possible to reduce the risk of unrealistic reconciliation.

In one example, the described solution may consider all of the modeled measurements, models and parameters of the system and take account of their respective uncertainties in order to provide a single estimate of each measurement or each computable parameter with a computed uncertainty guaranteeing the quality of the final value. The redundancy of the measurements is then a key factor. The reconciliation of several models and measurements constitutes a sort of advanced virtual counter. In order to eliminate interpretation errors by the computers used in most counters, the method uses the wrong measurements from each counter and clearly defined equations that are reconciled with the other data of the system. To that end, the DVR methodology is used: the idea of DVR is to use the redundancy of data from a system as a source of information to correct the measurements. Each measurement is corrected as little as possible, such that the corrected values meet all of the constraints of the process. The proposed method works primarily in steady state. However, some transient states due to operations may be detected automatically (e.g., starting of a well), and either the measurements and models influenced by these states are then removed from the reconciliation, or the associated uncertainties are then increased so that the optimizer understands that these data are not in their typical precision range. Lastly, in one example, the DVR methodology used manages the retention times of the production fluids when the lines are long: thus, the flow rates at the surface treatment at time t will be reconciled with flow rates at the wells at time t−2 h, for example. In other words, in one example, the method may ensure that the data determined in S10 and S12, and therefore involved in S20, are all relative to the provision of different flow rate estimates, these different estimates pertaining to the flow rate at a same moment.

The method may comprise a detection of any reconciled datum for which the difference between its value before reconciliation and its reconciled value exceeds a threshold depending on the uncertainty relative to the reconciled datum. Thus, the method detects the data determined in S10 (and potentially in S12) that are too remote from their reconciled value, the remoteness being considered based on the uncertainty relative to the datum. The DVR process can then be reiterated in S20, removing (each time) the reconciled datum with the greatest weight in the value of the cost function. This reiteration can be done as long as the value of the cost function is above a predetermined threshold. This makes it possible to refine the estimate of the flow rate, by leaving out the data determined in S10 and/or S12 as being excessively unreliable.

In one example, the reconciliation is done with all of the data of the model and their uncertainties. If a datum is corrected in an interval twice as high as its original uncertainty, it is detected and therefore considered suspicious. Another detection may be initiated when the sum of the penalties of the reconciliation exceeds a defined value. If this detection is activated, the most penalized datum will be muted and a new reconciliation will be done without incorporating it. When this other detection is not activated, the reconciliation is completed, but displays a high penalty. In this case, to determine which measurement is erratic, a detailed analysis can be done by giving priority to looking at the data showing the largest penalties. In one example, the method therefore makes it possible to monitor and track all of the sensors remotely (i.e., even in another location), identify faulty sensors remotely, and track the quality of the measurements using computed uncertainties. In one example, the method can also compute replacement values in the locations where the sensors are faulty and in the locations where no sensor has been placed. In one example, the method can use the raw measurements from the counters to avoid interpretation errors by the computers. In one example, the method can supply values and validated uncertainties to all data of the system. In one example, the method allows more reliable reallocation of the production by lines and by wells.

The data involved in the DVR process and determined in S10 and S12 will now be discussed.

For each considered device of a production well, these data are the baseline data that allow the device to supply an estimate of the flow rate in the line. A same datum may constitute a baseline datum for several devices. “Baseline datum” will be understood by one skilled in the art as a fundamental variable making it possible to compute a flow rate based on physical models, for example thermodynamic and/or mechanical models. This comprises measurements from sensors, predetermined hypotheses, for example fluid hypotheses, and/or values of calibration parameters (for example, physical, mechanical or chemical properties of the devices). The data determined in S10 comprise measurements taken in different locations of the site, for example pressure, temperature or flow rate measurements. The data determined in S12 comprise measurements taken at the test separator, in particular the measurements from the sensors of the metric counters of the test separator.

FIG. 5 shows an example well 100 on which the method can be carried out. The well 100 comprises several devices each adapted for providing a different estimate of the flow rate in the well, which comprise the metric counter 102, the chokes 104 (some of which are connected to a methanol injector 105 and/or only supply the flow rate indirectly), the device 106 associated with an inflow module (with perforations 107 in the tank 109), and the pipe 108, only one segment of which is shown in the figure. This example illustrates a production well with all of the standard devices. Several production wells of this type can be connected to one test separator to carry out the method of FIG. 1. The method has been tested on an installation comprising such production wells, and proved effective to improve the supplied estimate of the flow rate (for example, by the metric counter alone).

The operation of these different devices, although known by those skilled in the art, will now be outlined. The various data set out below and making it possible to obtain an estimate of the flow rate for each device can be involved, in whole or at least in part, in the DVR process. Indeed, below, we outline the respective data, for each device, to be determined in S10 (those which may be involved in the DVR process, the others being considered constant or accurate, and therefore generally not being involved). We also outline the sequence of computations making it possible to arrive at the flow rate estimate. For the test separator, the data determined in S12 may correspond to the details provided below for a multiphase metric counter, with the exception of the adaptation that the test separator in fact comprises several single-phase counters. The set of all of the computations make up the DVR constraint. The quality condition between the sum of the estimates by well and the estimate of the cumulative flow rate for all of the wells is driven by the use of an equation linking the variables in the underlying optimization of the DVR described above. The equality constraint between the different estimates of the flow rate of the fluid in a same well is supported by the use of the same variable to denote this flow rate (Qtot above). It should be noted that respective data for some devices also pertain to the metric counter, such that they are only determined once and they are represented by the same variable in the optimization underlying the DVR. The example where the DVR is done based on all of these models allows as accurate as possible an estimate of the flow rate after reconciliation.

As previously stated, the measurement done by the sensor of a metric counter on the fluid is an electric, nuclear and/or optical measurement. Thus, a metric counter can for example be adapted for estimating the flow rate from permittivity, conductivity, gamma attenuation (for several energy levels, as previously explained), dP (pressure drop), pressure and temperature measurements. These measurements can then all be involved in the DVR process. Furthermore, the data relative to the metric counter can also comprise predetermined fluid hypotheses as previously explained, such as the GOR (tank fluid composition in tank), salinity, calibration parameters (gamma attenuations, in the case of a multiphase counter: oil/water/gas at different energy levels and oil and gas permittivity references). These hypotheses can also then be involved in the DVR process. A metric counter also bases itself on constants to estimate the flow rate, and these constants are not necessarily involved in the DVR process. These are oil and gas permittivity equations (oil and gas reference function, Pressure, Temperature), the gas phase composition, the oil phase composition, the Venturi geometry, and the water conductivity equation (salinity function, Temperature, Pressure). Thus, the model of a multiphase metric counter can compute oil/gas/water fractions according to a function f(permittivity or conductivity, gamma attenuation, Pressure, Temperature, GOR, Salinity, oil composition, gas composition, oil permittivity, gas permittivity, oil and gas permittivity equation or water conductivity equation, oil/water/gas gamma attenuation) as previously indicated. The model of the multiphase metric counter can also compute oil/gas/water densities according to functions f(oil composition, gas composition, GOR, salinity, Pressure, Temperature). The model of the multiphase metric counter can next compute a mixture density according to a function f(oil/water/gas densities, oil/water/gas fractions), then a total flow rate Qtot according to a function f(dP, mixture density, Venturi geometry), then flow rates by Qoil/Qwater/Qgas phase according to a function f(Qtot, oil/water/gas fraction). The preceding explanations relate to a multiphase counter in a well, but the principle is similar for each single-phase counter of the test separator. The test separator thus makes it possible to determine different estimates of cumulative flow rates by phase Qoil′/Qwater′/Qgas′ independently of one another according to this principle. The DVR process S20 implements the equality constraint via an equation of the type Qoil′=Sum of the Qoil supplied for each well, Qwater′=Sum of the Qwater supplied for each well, and Qgas′=Sum of the Qgas supplied for each well.

As described below, it is also possible to adjust the redundancy with other devices adapted for providing an estimate of the flow rate by well. The estimates provided below do not supply a result by phase, but only a global Qtot for all three phases each time. The equality constraint between the estimated flow rates for a same well is supported by the use of the same variable Qtot in the DVR process.

A choke is for example adapted for providing an estimate of the flow rate of the fluid in a well based on a valve opening and a measurement done by at least one sensor on the fluid supplying the pressure losses associated with the valve opening (typically two pressure sensors each supplying a pressure measurement that will be compared). Thus, the measured data are the pressure upstream from the choke, the pressure downstream from the choke, and the subsea valve opening percentage (subsea choke opening %). The hypotheses are the max Cv and the topside valve opening percentage (topside choke opening %). All of these data can be involved in the DVR process. The model can also be based on a constant not involved in the DVR: the choke curve. Thus, the choke makes it possible to provide the Qtot flow rate according to a function f(upstream Pressure, downstream Pressure, choke opening %, mixture density, max Cv, choke curve).

The device associated with the inflow module is the part of the production well at the tank comprising the perforations/openings/inlets for receiving the hydrocarbon. The inflow module is the virtual inflow device for the hydrocarbon in the well. A flow rate estimating model is supplied for this virtual device through sensors belonging to the mentioned part of the production well. Thus, this part of the well may conceptually be “associated” with the inflow module and is thus adapted for supplying an estimate of the fluid flow rate in the well according to the model. The estimate is done based on a pressure measurement done by at least one sensor on the fluid and physicochemical properties of the fluid, all of this data being able to be involved in the DVR process. The inflow module makes it possible to estimate the flow rate based on measurements, which are the well bottom pressure (potentially the pressure at the inlet of the pipe if the pipe is likened to the well) and the initial tank pressure. The model also involves a hypothesis that replaces the initial tank pressure during production, i.e., the interpolated tank pressure (supplied by the geologist). These data are involved by the DVR. Constants that are not involved also come into play: the layer-hole bonding properties. Thus, it is possible to estimate the flow rate of the fluid Qtot according to a function f(well bottom Pressure, tank Pressure (initial or interpolated), layer-hole bonding properties).

The pipe is adapted for providing an estimate of the flow rate of the fluid in the well based on geometric and/or mechanical properties of the pipe and a measurement done by at least one sensor on the fluid supplying the pressure losses associated with the geometric and/or mechanical properties of the pipe. Thus, input Pressure and output Pressure are measured, and/or input Temperature and output Temperature, and mechanical property and thermal property hypotheses of the pipe are formed. All of these data can be involved in the DVR. A non-involved constant, i.e., the geometry of the pipe, then serves as a basis for recursively performing a series of computations making it possible to converge toward a flow rate estimate. These computations are those of the speed according to a function f(Qtot, pipe geometry), flow conditions according to a function f(oil composition, gas composition, mixture density, speed), the total flow rate according to a function f(input Pressure, output Pressure, mixture density, pipe geometry, mechanical properties, flow conditions) or according to a function f(input Temperature, output Temperature, mixture density, pipe geometry, thermal properties, flow conditions). This sequence of computations is repeated recursively, as indicated above.

Of course, the example of FIG. 5 in theory makes it possible to reconcile many data, with a redundancy having a degree equal to the number of available estimates. However, for the wells where fewer data are collected, or when some data are omitted as indicated above, it is possible to reconcile only a combination of the models described above. As long as one has at least one estimate per well, and a test separator linking the wells, the method of FIG. 1 improves the estimate by well. 

1. A method for producing hydrocarbons on a line of a hydrocarbon production installation comprising at least two production wells, each production well comprising at least one device adapted for providing an estimate of the flow rate of a fluid in the well based on respective data, the wells being connected to a test separator adapted for providing an estimate of the cumulative flow rate of the fluid for all of the wells based on respective data, wherein the method comprising, during the production: determining the data respective to the provision of an estimate of the flow rate by the device of each well; determining the data respective to the provision of an estimate of the cumulative flow rate by the test separator; and a data reconciliation and validation process involving the determined data, the reconciliation being conditioned by an at least substantial equality between the sum of the estimates of the flow rate of the fluid to be provided by the device of each well and the estimated cumulative flow rate to be provided by the test separator.
 2. The method according to claim 1, wherein the data respective to the provision of an estimate of the cumulative flow rate via the test separator comprise at least one measurement done by a sensor of the test separator.
 3. The method according to claim 2, wherein the test separator comprises at least one metric counter, the sensor taking the measurement being a sensor of the metric counter.
 4. The method according to claim 3, wherein the test separator comprises several single-phase metric counters each adapted for providing an estimate of the flow rate of a respective phase of the fluid based on a measurement taken by a respective sensor.
 5. The method according to claim 1, wherein the fluid comprises a water phase, a gas phase and an oil phase.
 6. The method according to claim 1, wherein at least one production well comprises a metric counter adapted for providing an estimate of the flow rate of the fluid in the well based on respective data including a measurement done by at least one sensor of the metric counter on the fluid.
 7. The method according to claim 6, wherein a production well comprising a metric counter further comprises at least one other device adapted for providing an estimate of the flow rate of the fluid in the well based on respective data that are determined and involved in the data reconciliation and validation process.
 8. The method according to claim 7, wherein the other device is a choke, a device associated with an inflow module, and/or a pipe.
 9. A non-transitory data storage memory having recorded thereon a computer program comprising instructions for carrying out a method for producing hydrocarbons on a line of a hydrocarbon production installation comprising at least two production wells, each production well comprising at least one device adapted for providing an estimate of the flow rate of a fluid in the well based on respective data, the wells being connected to a test separator adapted for providing an estimate of the cumulative flow rate of the fluid for all of the wells based on respective data, the method comprising, during the production: determining the data respective to the provision of an estimate of the flow rate by the device of each well; determining the data respective to the provision of an estimate of the cumulative flow rate by the test separator; and a data reconciliation and validation process involving the determined data, the reconciliation being conditioned by an at least substantial equality between the sum of the estimates of the flow rate of the fluid to be provided by the device of each well and the estimated cumulative flow rate to be provided by the test separator.
 10. A system adapted for communicating with devices comprised in at least two production wells of a hydrocarbon production line, the devices being adapted for providing an estimate of the flow rate of the fluid in the well based on respective data, and with a test separator connected to the wells and adapted for providing an estimate of the cumulative flow rate of the fluid for all of the wells based on respective data, the system comprising a memory having recorded a program comprising instructions for carrying out a method for producing hydrocarbons on the hydrocarbon production line, the method comprising, during the production: determining the data respective to the provision of an estimate of the flow rate by the device of each well; determining the data respective to the provision of an estimate of the cumulative flow rate by the test separator; and a data reconciliation and validation process involving the determined data, the reconciliation being conditioned by an at least substantial equality between the sum of the estimates of the flow rate of the fluid to be provided by the device of each well and the estimated cumulative flow rate to be provided by the test separator.
 11. (canceled) 